This work provides the community with a timely comprehensive review of backdoor attacks
and countermeasures on deep learning. According to the attacker's capability and affected …

Abstract The Internet of Things (IoT) envisions a smart environment powered by connectivity
and heterogeneity where ensuring reliable services and communications across multiple …

Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to
displace many in the professional artist community. In particular, models can learn to mimic …

Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …

With the success of deep learning algorithms in various domains, studying adversarial
attacks to secure deep models in real world applications has become an important research …

Today's proliferation of powerful facial recognition systems poses a real threat to personal
privacy. As Clearview. ai demonstrated, anyone can canvas the Internet for data and train …

Deep neural networks (DNNs) have been proven vulnerable to backdoor attacks, where
hidden features (patterns) trained to a normal model, which is only activated by some …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

The unprecedented success of deep neural networks in many applications has made these
networks a prime target for adversarial exploitation. In this paper, we introduce a benchmark …

Natural language processing (NLP) systems have been proven to be vulnerable to backdoor
attacks, whereby hidden features (backdoors) are trained into a language model and may …